System for determining content topicality, and method and program thereof

ABSTRACT

A system for determining content topicality comprises a feature value extracting means which extracts feature values of a plurality of contents from the contents; a content grouping means which compares the feature values of the plurality of contents extracted by the feature value extracting means, obtains identical contents and derived contents created using the contents, both of which are included in the plurality of contents, and groups the identical/derived contents, thereby computing identical/derived content grouping information; and a topicality determining means which totals viewing frequencies of the contents determined to be the identical/derived contents from viewing history information and the identical/derived content grouping information relating to the plurality of contents, computes total viewing frequencies of each of the identical/derived contents, and determines the topicality of the identical/derived contents according to the total viewing frequency.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a National Stage of International Application No.PCT/JP2009/060908 filed Jun. 16, 2009, claiming priority based onJapanese Patent Application No. 2008-167344 filed. Jun. 26, 2008, thecontents of all of which are incorporated herein by reference in theirentirety.

TECHNICAL FIELD

The present invention relates to a content topicality determinationsystem, and a method and a program therefor.

BACKGROUND ART

Recently, many video hosting sites are rising, and the environment inwhich various videos can be viewed via Internet is being arranged.However, it is difficult to search out useful contents rich in thetopicality because a large number of the video images are contributed.For this, when a viewing frequency representing an extent to which eachcontent is viewed is grasped, the topicality can be determined from theviewing frequency.

One example of the system for distributing the contents over a network,and simultaneously therewith, measuring the viewing frequency isdescribed in Patent Literature 1. In this system, a receiving terminalside thereof is provided with a means for measuring the viewing data,and when a user views the content that is distributed from a contentserver, the means measures its viewing data, and collectively transmitsthe viewing data and attribute information of the user to the contentserver. The content server collects the viewing data to be sent from theuser, and calculates the viewing frequency data content by content.

However, employing the viewing frequency data as disclosed inJP-P2007-202054A and trying to determine the topicality of the contentscauses a problem.

SUMMARY OF INVENTION Technical Problem

A first controversial problem is that the topical contents cannot becorrectly extracted based upon the viewing frequency disclosed in thePatent literature 1.

The reason is that the viewing frequency is calculated for each ID(identification information) for identifying the content. For this, theviewing frequency can be calculated only for each of individual contentswhen the ID management of the content is not accurately carried out andyet a plurality of IDs are given to the identical contents, or when theidentical contents are distributed from a plurality of the video imagesites each having a different policy of giving the content ID.

Thus, the user's viewing is dispersed all the more as the number of theIDs given to the identical contents is increased, which causes theviewing frequency of the content for each one identification ID todecline, and the viewing frequency to hardly reflects the topicality ofthe content accurately. Further, when the content that can be producedby derivation of a certain content exists (when one part of the video ofa certain news content is utilized for other programs, or the like), theoriginal content is included in the above derived content (there is notonly the case that the original content is simply included therein, butalso the case that the original content subjected to modifications tosome extent such as character superimposition and a change in size/colorof the image is included therein). As a result, the viewing of thederived content leads to the viewing as well of the original content.

Accordingly, in a case of determining the topicality, it is important totake the viewing frequency, which includes the viewing frequency as wellof the derived content, into consideration.

Thereupon, the present invention has been accomplished in considerationof the above-mentioned problems, and an object thereof is to provide acontent topicality determination system capable of adequatelydetermining the topicality of the content, a method and a programtherefor.

Solution to Problem

The present invention for solving the above-mentioned problems is acontent topicality determination system including: a feature extractionmeans for extracting features of the contents from a plurality of thecontents; a content grouping means for collating the features of aplurality of the contents extracted by the foregoing feature extractionmeans with each other, obtaining the identical contents and the derivedcontents produced by using the above identical contents to be includedin the foregoing plurality of the contents, grouping theidentical/derived contents, and calculating identical/derived contentgrouping information; and a topicality determination means for totalingviewing frequencies of the contents determined to be theidentical/derived contents from viewing history information of theforegoing plurality of the contents and the foregoing identical/derivedcontent grouping information, calculating a total viewing frequency foreach identical/derived content, and determining the topicality of theforegoing identical/derived content based upon the foregoing totalviewing frequency.

The present invention for solving the above-mentioned problems is acontent topicality determination method including: a feature extractionstep of extracting features of the contents from a plurality of thecontents; a content grouping step of collating the features of theforegoing plurality of the extracted contents with each other, obtainingthe identical contents and the derived contents produced by using theabove identical contents to be included in the foregoing plurality ofthe contents, grouping the identical/derived contents, and calculatingidentical/derived content grouping information; and a topicalitydetermination step of totaling viewing frequencies of the contentsdetermined to be the identical/derived contents from viewing historyinformation of the foregoing plurality of the contents and the foregoingidentical/derived content grouping information, calculating a totalviewing frequency for each identical/derived content, and determiningthe topicality of the foregoing identical/derived content based upon theforegoing total viewing frequency.

The present invention for solving the above-mentioned problems is acontent topicality determination program for causing an informationprocessing apparatus to execute: a feature extraction process ofextracting features of the contents from a plurality of the contents; acontent grouping process of collating the features of the foregoingplurality of the extracted contents with each other, obtaining theidentical contents and the derived contents produced by using the aboveidentical contents to be included in the foregoing plurality of thecontents, grouping the identical/derived contents, and calculatingidentical/derived content grouping information; and a topicalitydetermination process of totaling viewing frequencies of the contentsdetermined to be the identical/derived contents from viewing historyinformation of the foregoing plurality of the contents and the foregoingidentical/derived content grouping information, calculating a totalviewing frequency for each identical/derived content, and determiningthe topicality of the foregoing identical/derived content based upon theforegoing total viewing frequency.

Advantageous Effect of Invention

The present invention is capable of adequately determining thetopicality of the content.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating a configuration of the contenttopicality determination system in an exemplary embodiment of thepresent invention.

FIG. 2 is a view illustrating one example of grouping the contents eachhaving a time axis.

FIG. 3 is a block diagram illustrating the exemplary embodiment of atopicality determination means 102 of FIG. 1.

FIG. 4 is a block diagram illustrating another exemplary embodiment ofthe topicality determination means 102 of FIG. 1.

FIG. 5 is a flowchart illustrating an operation of the entirety of thecontent topicality determination system in this exemplary embodiment.

FIG. 6 is a flowchart illustrating an operation of the topicalitydetermination means 102 shown in FIG. 3.

FIG. 7 is a flowchart illustrating an operation of the topicalitydetermination means 102 shown in FIG. 4.

DESCRIPTION OF EMBODIMENTS

The content topicality determination system of this exemplary embodimentincludes: a feature extraction means (100 of FIG. 1) for extracting thefeatures of the contents from a plurality of the contents; a contentgrouping means (101 of FIG. 1) for collating the features of a pluralityof the contents extracted by the feature extraction means with eachother, obtaining the identical contents and the derived contentsproduced by using the above identical contents to be included in aplurality of the contents, grouping the identical/derived contents, andcalculating identical/derived content grouping information; and atopicality determination means (102 of FIG. 1) for totaling viewingfrequencies of the contents determined to be the identical/derivedcontents from viewing history information of a plurality of the contentsand the identical/derived content grouping information, calculating atotal viewing frequency for each identical/derived content, anddetermining the topicality of the foregoing identical/derived contentbased upon the total viewing frequency.

Also when a plurality of different IDs are given to the identicalcontents, or when the above content is employed as one part of the othercontents, the content topicality determination system configured in sucha manner allows these contents to be grouped and the viewing frequencyto be calculated for the group. Determining the topicality based uponthis viewing frequency makes it possible to precisely determine thetopicality of the content, and accomplish an object of the presentinvention also when a plurality of different IDs are given to theidentical contents, or when the above content is employed as one part ofthe other contents.

Next, the exemplary embodiment of the present invention will beexplained in details by making a reference to the accompanied drawings.

FIG. 1 is a view illustrating the content topicality determinationsystem in this exemplary embodiment of the present invention. Uponmaking a reference to FIG. 1, the content topicality determinationsystem in this exemplary embodiment is configured of a featureextraction means 100, an identical/derived content grouping means 101, atopicality determination means 102, a content viewing means 104, acontent storage means 105, and a content viewing history storage means106.

The content storage means 105, which stores a plurality of the contents,is connected to the feature extraction means 100 and the content viewingmeans 104. The feature extraction means 100, into which the contents areinputted from the content storage means 105, obtains the features forthe contents and outputs the features to the identical/derived contentgrouping means 101. The identical/derived content grouping means 101,into which the features to be outputted from the feature extractionmeans 100 are inputted, obtains content link information representing alink relation between the features, outputs it as grouping informationto the topicality determination means 102. The topicality determinationmeans 102, into which the grouping information is inputted from theidentical/derived content grouping means 101, and the content viewinghistory information is inputted from the content viewing history storagemeans 106, respectively, generates and outputs topical contentinformation. The content viewing means 104, into which the contents areinputted from the content storage means 105, outputs the viewing historyinformation to the content viewing history storage means 106.

Next, an operation of the content topicality determination system shownin FIG. 1 will be explained.

The contents are stored in the content storage means 105. Herein, theso-called content refers to, for example, a digitalized multimediacontent, and the digitalized picture, video and music, a combinationthereof, and the like fall under the content. Additionally, the contentcould be not only a content produced by a professional such as abroadcast program, but also a so-called CGM (Consumer Generated Media),being a content produced by a consumer. Hereinafter, while the videoimage content is exemplified for explanation, the situation is similarlyapplicable to the music, the picture, and the like.

Further, while the content storage means 105 was explained in such amanner that the contents were stored in one location for convenience,the contents may be dispersedly stored in a plurality of the storages.For example, for a plurality of the video image hosting sites overInternet, the video images may be stored for each site. Further, also ineach site, the contents may be dispersed and stored in a plurality ofthe storages. The contents stored in the content storage means 105 areinputted into the feature extraction means 100.

The feature extraction means 100 performs the feature extraction foreach of the contents to be inputted. With the case of the picture, thefeature is a visual feature such as color, pattern, and shape, and forexample, the feature standardized by ISO/IEC 15938-3 can be employed.With the case of the music, the feature is an audio feature such as apower and a frequency component of sound, and for example, the featurestandardized by ISO/IEC 15938-4 can be employed. With case of the video,besides the foregoing visual feature, the visual feature expressive ofmotion can be also employed, and for example, the feature standardizedby ISO/IEC 15938-3 can be employed. Further, the foregoing audio featuremay be employed, and both of the visual feature and the audio featuremay be employed. The extracted feature of each of the contents isoutputted to the identical/derived content grouping means 101.

The identical/derived content grouping means 101 collates the featuresof the contents to be inputted with each other, regards the contents ofwhich the similarity between the features is large as contents eachhaving identical details, and groups them. Specifically, theidentical/derived content grouping means 101 calculates the similarity(or a distance) between the features of a certain two contents, andgroups the above two contents when the similarity is equal to or morethan a threshold (equal to or less than a threshold with the case of thedistance).

With the case of the picture, comparing the entireties of the picturepartners with each other and performing the similarity calculation atthe moment of calculating the similarity makes it possible to group theidentical pictures. Further, the similarity may be calculated bycollating region partners of one part of the picture with each other. Inthis case, the other images that can be obtained by using a certainpicture (for example, the images that can be obtained by framing thepicture, and the image that can be obtained by affixing a certainpicture to another picture), namely, the derived contents can be alsogrouped. On the other hand, with the case of the contents each having atime axis such as the video and the music, the identical/derived contentgrouping means 101 groups the contents in terms of each time section (asection length is arbitrary). For example, when it is assumed that acollation between each of a content A, a content B, a content C and acontent D, and the other as shown in FIG. 2 is carried out, the timesection partners shown with oblique striped lines, and the time sectionpartners shown with vertical striped lines are grouped, respectively.The grouping information obtained in such a manner is outputted to thetopicality determination means 102.

At the moment that the user selects and views the contents, the contentsstored in the content storage means 105 are also inputted into thecontent viewing means 10. In the content viewing means 104, the userreproduces and views the contents. Simultaneously, the content viewingmeans 104 records the viewing history of the content. At this moment,with regard to the viewing history, the content viewing means 104 mayrecord only whether the content has been reproduced, or when the userhas not initiated the viewing at the beginning, the content viewingmeans 104 skips over the beginning part and records only the informationof the actually reproduced location. Further, when the section in whichcontent has been fast-forwarded exists, the content viewing means 104records the above section information as well. The by-content viewinghistory information is outputted to the content viewing history storagemeans 106.

The viewing history information to be inputted is stored in the contentviewing history storage means 106. This viewing history information aswell may be dispersedly stored in a plurality of the storages similarlyto the case of the content storage means 105. The viewing historyinformation is inputted into the topicality determination means 102.

The topicality determination means 102 calculates the viewing frequencyof each of the contents from the grouping information and the viewinghistory information. Herein, the topicality determination means 102calculates the viewing history information content by content, andcalculates a total viewing frequency by totaling the viewing historieswithin the group by use of the grouping information. And, the topicalitydetermination means 102 determines the topicality of the content basedupon this total viewing frequency, and outputs it as topicalityinformation. The details of an operation of the topicality determinationmeans 102 will be described later.

Next, an operation of the entirety of the content topicalitydetermination system will be explained by employing a flowchart.

FIG. 5 is a flowchart representing a flow of the entirety of a processof the content topicality determination system in this exemplaryembodiment shown in FIG. 1.

At first, in a step S500, the by-content feature is extracted. Thedetails of the extraction are ones described in the feature extractionmeans 100. Next, in a step S501, the extracted features are collated interms of the content, the contents are grouped, and the groupinginformation is obtained. The details of the grouping are ones describedin the identical/derived content grouping means 101. And, in a stepS502, the topicality of the content is determined by employing thegrouping information and the viewing history information, and thetopicality information is calculated.

Next, the exemplary embodiment of the topicality determination means 102will be described in details.

FIG. 3 is a view representing one exemplary embodiment of the topicalitydetermination means 102.

FIG. 3 shows a configuration that is comprised of a viewing frequencycalculation means 200, a total viewing frequency calculation means 201,and a topicality index calculation means 202. The viewing frequencycalculation means 200, which has the viewing history information as aninput, outputs the by-content viewing frequency information to the totalviewing frequency calculation means 201. The total viewing frequencycalculation means 201, which has the grouping information, and theviewing frequency information to be outputted from the viewing frequencycalculation means 200 as an input, respectively, outputs the totalviewing frequency to the topicality index calculation means 202. Thetopicality index calculation means 202, which has the total viewingfrequency as an input, calculates and outputs the topicalityinformation.

Next, an operation of the topicality determination means 102 of FIG. 3will be explained.

At first, the viewing frequency calculation means 200 calculates theviewing frequencies of individual contents from the viewing historyinformation. The obtained by-content viewing frequency information isoutputted to the total viewing frequency calculation means 201.

The total viewing frequency calculation means 201 calculates the totalviewing frequency, being a viewing frequency as the entirety of thegrouped contents, by totaling the viewing frequencies of individualcontents. On the other hand, with the case of the contents each having atime axis such as the video and the music, the total viewing frequencycalculation means 201 calculates the total viewing frequency of thecontents in terms of each time section (a section length is arbitrary).

For example, with the case of an example of FIG. 2, when it is assumedthat the viewing frequencies of individual contents to be obtained inthe viewing frequency calculation means 200 are not dependent upon thetime, and are N_(A), N_(B), N_(C), and N_(D), respectively, the totalviewing frequency of the part shown with the oblique lines becomes oneshown by Equation 1, and the total viewing frequency of the part shownwith the vertical striped lines becomes one shown by Equation 2.N_(A)+N_(B)+N_(C)  Equation 1N_(A)+N_(B)+N_(C)+N_(D)  Equation 2

On the other hand, in a case where the viewing frequencies of respectivecontents differs from each other time by time due to the partialreproduction of the content, when it is assumed that the viewingfrequencies for media times (relative times measured from the heads ofthe contents) of a content A, a content B, a content C, and a content Dare N_(A)(t), N_(B)(t), N_(C)(t), and N_(D)(t), respectively, the totalviewing frequency of the part shown with the oblique lines becomes oneshown by Equation 3, and the total viewing frequency of the part shownwith the vertical striped lines becomes one shown by Equation 4.N_(A)(t)+N_(B)(t)+N_(C)(t+t₁)  Equation 3N_(A)(t)+N_(B)(t)+N_(C)(t+t₁)+N_(D)(t−t₃+t₂)  Equation 4

The total viewing frequency calculated in such a manner is outputted tothe topicality index calculation means 202. Or, the viewing frequenciesof the respective contents may be totaled by performing the weighting asto whether the content is an original content or a derived content. Or,the viewing frequencies of the respective contents may be totaled byadding a reliability of the site in which each content exists, andperforming the weighting that is dependent upon the site. In this case,the total viewing frequency of the part shown with the oblique linesbecomes one shown by Equation 5 when the viewing frequency is notdependent upon the time, and becomes one shown by Equation 6 when theviewing frequency is dependent upon the time. Where, each of W_(A),W_(B), and W_(C) is a weighting factor.W_(A)N_(A)+W_(B)N_(B)+W_(C)N_(C)  Equation 5W_(A)N_(A)(t)+W_(B)N_(B)(t)+W_(C)N_(C)(t+t₁)  Equation 6

Additionally, the viewing frequency of the content of which the viewingtime is nearer to the current time may be weighted more largely andcalculated. For example, the control such that the weight of the viewingfrequency of the content viewed today is 1, that of the viewingfrequency of the content viewed k days ago is 1−k/N, and that of theviewing frequency of the content viewed N days ago is zero, namely thatof the viewing frequency of the above content is not counted isthinkable. This allows the latest viewing frequency to be regarded asimportant, thereby making a possible to extract more seasonable andtopical contents.

The topicality index calculation means 202 determines the topicality ofthe content or the by-time topicality of the content based upon thetotal viewing frequency. The simplest way is that it may be jugged thatthe larger the total, the larger the topicality. That is, the totalviewing frequency can be employed as an index of the topicality as itstands.

Next, an operation of the topicality determination means 102 will beexplained by using a flowchart.

FIG. 6 is a flowchart of the entirety of the process of the topicalitydetermination means 102 shown in FIG. 3.

At first, in a step S550, the viewing frequency is calculated for eachcontent unit. Next, in a step S551, the viewing frequencies for eachcontent unit are totaled in terms of the grouped content to calculatethe total viewing frequency. Finally, in a step S552, the topicalityindex is calculated from the total viewing frequency, and outputted.

Next, another exemplary embodiment of the topicality determination means102 will be described in details.

FIG. 4 is a view representing another exemplary embodiment of thetopicality determination means 102.

FIG. 4 shows a configuration that is comprised of a viewing frequencycalculation means 200, a total viewing frequency calculation means 201,a topicality index calculation means 202, and a valid viewing sectiondetermination means 250. An input into the valid viewing sectiondetermination means 250 is the viewing history information, and anoutput from it is inputted into the viewing frequency calculation means200. A configuration other than it is similar to that of the topicalitydetermination means 102 of FIG. 3.

Next, an operation of the topicality determination means 102 of FIG. 4will be explained.

Operations of the viewing frequency calculation means 200, the totalviewing frequency calculation means 201, and the topicality indexcalculation means 202 are similar to those of FIG. 3. Herein, anoperation of the valid viewing section determination means 250 will bedescribed.

The viewing history information is inputted into the valid viewingsection determination means 250. The valid viewing section determinationmeans 250 determines which history, out of the inputted viewinghistories, is unsuitable for the viewing, and deletes the historydetermined to be unsuitable. For example, when the reproduction time ofthe content is very short, a possibility that it is not that the abovecontent is viewed, but that the above content accidentally attracts anattention when it is selected by zapping is high. Or, the case ofcarrying out the special reproduction such as fast-forwarding alsodiffers from the case of the normal viewing. Thus, these logs areexcluded, and the remaining histories are outputted to the viewingfrequency calculation means 200.

FIG. 7 is a flowchart of the entirety of the process of the topicalitydetermination means 102 shown in FIG. 4.

Steps other than the step S553 of determining the valid viewing sectionlisted in the first place are similar to those of a flowchart of FIG. 6.The step of determining the valid viewing section (step S553) determineswhich history, out of the inputted viewing histories, is unsuitable forthe viewing, and deletes the history determined to be unsuitable. Thedetermination subsequent hereto is similar to that of a flowchart ofFIG. 6.

This exemplary embodiment makes it possible to precisely determine thetopicality of a certain section of the content because a configurationis made so that the identical/derived contents are automatically foundout from among a plurality of the contents, and grouped, and thetopicality is determined by totaling the viewing frequencies within thegroup section by section.

The reason is that the video images are collated with each other foreach section unit, and not only the identical contents, each of whichhas an different ID given, but also the contents, which can be producedby derivation of a certain content, can be grouped as contents eachhaving identical details, thereby making it possible to evaluating thetopicality for the above group by use of the entirety of the viewingfrequencies of individual contents.

Additionally, while each part was configured with hardware in theabove-mentioned exemplary embodiment, it may be configured with theinformation processing apparatus such as CPU that operates under aprogram. In this case, the program causes the information processingapparatus such as CPU to execute the above-described operation.

The first mode of the present invention is characterized in that acontent topicality determination system comprising: a feature extractionmeans for extracting features of contents from a plurality of thecontents; a content grouping means for collating the features of aplurality of the contents extracted by said feature extraction meanswith each other, obtaining the identical contents and the derivedcontents produced by using the above identical contents to be includedin said plurality of the contents, grouping the identical/derivedcontents, and calculating identical/derived content groupinginformation; and a topicality determination means for totaling viewingfrequencies of the contents determined to be the identical/derivedcontents from viewing history information of said plurality of thecontents and said identical/derived content grouping information,calculating a total viewing frequency for each identical/derivedcontent, and determining topicality of said identical/derived contentbased upon said total viewing frequency.

The second mode of the present invention, in the above-mentioned mode,is characterized in that said content has a time axis; wherein saidcontent grouping means groups the identical/derived contents for eachtime section by said collation, and calculates said identical/derivedcontent grouping information; and wherein said topicality determinationmeans calculates said total viewing frequency for each time section, anddetermines the topicality for each time section.

The third mode of the present invention, in the above-mentioned mode, ischaracterized in that said content is music or video.

The fourth mode of the present invention, in the above-mentioned mode,is characterized in that said feature of the content includes at leastone of a visual feature and an audio feature.

The fifth mode of the present invention, in the above-mentioned mode, ischaracterized in that said topicality determination means determinesthat the content of which said total viewing frequency is large is atopical content.

The sixth mode of the present invention, in the above-mentioned mode, ischaracterized in that said topicality determination means determines thetopicality of the content by a time section.

The seventh mode of the present invention, in the above-mentioned mode,is characterized in that the topicality determination system comprisinga content viewing means for selecting the content from among saidplurality of the contents, viewing the content, and outputtingidentification information and a viewing section of the viewed contentas said content viewing history information.

The eighth mode of the present invention, in the above-mentioned mode,is characterized in that said topicality determination means: comprisesa valid viewing section determination means for determining only thehistory meeting a constant viewing condition to be valid from saidcontent viewing history information; and calculates the total viewingfrequency by using the section determined to be valid by said validviewing section determination means.

The ninth mode of the present invention is characterized in that acontent topicality determination method comprising: a feature extractionstep of extracting features of contents from a plurality of thecontents; a content grouping step of collating the features of saidplurality of the extracted contents with each other, obtaining theidentical contents and the derived contents produced by using the aboveidentical contents to be included in said plurality of the contents,grouping the identical/derived contents, and calculatingidentical/derived content grouping information; and a topicalitydetermination step of totaling viewing frequencies of the contentsdetermined to be the identical/derived contents from viewing historyinformation of said plurality of the contents and said identical/derivedcontent grouping information, calculating a total viewing frequency foreach identical/derived content, and determining topicality of saididentical/derived content based upon said total viewing frequency.

The tenth mode of the present invention, in the above-mentioned mode, ischaracterized in that said content has a time axis; wherein said contentgrouping step groups the identical/derived contents for each timesection by said collation, and calculates said identical/derived contentgrouping information; and wherein said topicality determination stepcalculates said total viewing frequency for each time section, anddetermines the topicality for each time section.

The eleventh mode of the present invention, in the above-mentioned mode,is characterized in that said content is music or video.

The twelfth mode of the present invention is characterized in that saidfeature of the content includes at least one of a visual feature and anaudio feature.

The thirteenth mode of the present invention is characterized in thatsaid topicality determination step determines that the content of whichsaid total viewing frequency is large is a topical content.

The fourteenth mode of the present invention, in the above-mentionedmode, is characterized in that said topicality determination stepdetermines the topicality of the content by a time section.

The fifteenth mode of the present invention, in the above-mentionedmode, is characterized in that the topicality determination methodcomprising a content viewing step of selecting the content from amongsaid plurality of the contents, viewing the content, and outputtingidentification information and a viewing section of the viewed contentas said content viewing history information.

The sixteenth mode of the present invention, in the above-mentionedmode, is characterized in that said topicality determination step:determines only the history meeting a constant viewing condition to bevalid from said content viewing history information; and calculates thetotal viewing frequency by using said section determined to be valid.

The seventeenth mode of the present invention is characterized in that acontent topicality determination program for causing an informationprocessing apparatus to execute: a feature extraction process ofextracting features of contents from a plurality of the contents; acontent grouping process of collating the features of said plurality ofthe extracted contents with each other, obtaining the identical contentsand the derived contents produced by using the above identical contentsto be included in said plurality of the contents, grouping theidentical/derived contents, and calculating identical/derived contentgrouping information; and a topicality determination process of totalingviewing frequencies of the contents determined to be theidentical/derived contents from viewing history information of saidplurality of the contents and said identical/derived content groupinginformation, calculating a total viewing frequency for eachidentical/derived content, and determining topicality of saididentical/derived content based upon said total viewing frequency.

Above, although the present invention has been particularly describedwith reference to the preferred embodiments and modes thereof, it shouldbe readily apparent to those of ordinary skill in the art that thepresent invention is not always limited to the above-mentionedembodiment and modes, and changes and modifications in the form anddetails may be made without departing from the spirit and scope of theinvention.

This application is based upon and claims the benefit of priority fromJapanese patent application No. 2008-167344, filed on Jun. 26, 2008, thedisclosure of which is incorporated herein in its entirety by reference.

INDUSTRIAL APPLICABILITY

The present invention is applicable to a field of determining thetopicality of the contents subscribed over the network section bysection. Further, the foregoing field is not limited to the network, andthe present invention is similarly applicable to the contents stored inan identical hard disc recorder so long as the identical/derivedcontents exist in it.

REFERENCE SIGNS LIST

-   -   100 feature extraction means    -   101 identical/derived content grouping means    -   102 topicality determination means    -   104 content viewing means    -   105 content storage means    -   106 content viewing history storage means    -   200 viewing frequency calculation means    -   201 total viewing frequency calculation means    -   202 topicality index calculating means    -   250 valid viewing section determination means

1. A content topicality determination system comprising: an informationprocessing apparatus; a feature extractor that inputs a plurality of thecontents from a content storage, extracts features for each of saidplurality of the contents; a content grouping unit that collates thefeatures of a plurality of the contents extracted by said featureextractor with each other, groups identical contents and derivedcontents produced by using the above identical contents to be includedin said plurality of the contents, based on the result of saidcollation, and calculates identical/derived content groupinginformation; and a topicality determination unit that inputs viewingfrequencies of the contents from a content viewing history storage,totals viewing frequencies of the contents determined to be theidentical/derived contents from said input viewing history informationof said plurality of the contents and said identical/derived contentgrouping information, calculates a total viewing frequency for eachidentical/derived content, and determines topicality of saididentical/derived content based upon said total viewing frequency.
 2. Acontent topicality determination system according to claim 1: whereinsaid content has a time axis; wherein said content grouping unit groupsthe identical/derived temporal segments included in said plurality ofthe contents based on the result of said collation, and calculates saididentical/derived content grouping information; and wherein saidtopicality determination unit calculates said total viewing frequencyfor each of said identical/derived temporal segments, and determines thetopicality for each of said identical/derived temporal segments.
 3. Acontent topicality determination system according to claim 2, whereinsaid content is music or video.
 4. A content topicality determinationsystem according to claim 3, wherein said feature of the contentincludes at least one of a visual feature and an audio feature.
 5. Acontent topicality determination system according to claim 1, whereinsaid topicality determination unit determines that the content of whichsaid total viewing frequency is large is a topical content.
 6. A contenttopicality determination system according to claim 5, wherein saidtopicality determination unit determines the topicality of the contentby a temporal segment.
 7. A content topicality determination systemaccording to claim 1, comprising a content viewing unit that selects thecontent from among said plurality of the contents, views the content,and outputs identification information and a viewing segment of theviewed content as said content viewing history information.
 8. A contenttopicality determination system according to claim 1, wherein saidtopicality determination unit: comprises a valid viewing sectiondetermination unit for determines only the history meeting a constantviewing condition to be valid from said content viewing historyinformation; and calculates the total viewing frequency by using thesection determined to be valid by said valid viewing sectiondetermination means.
 9. A content topicality determination method byusing a computer comprising: a feature extraction step of inputting aplurality of contents from a content storage, extracting features foreach of said plurality of the contents; a content grouping step ofcollating the features of said plurality of the contents extracted bysaid feature extractor with each other, grouping identical contents andderived contents produced by using the above identical contents to beincluded in said plurality of the contents, based on the result of saidcollation, and calculating identical/derived content groupinginformation; and a topicality determination step of inputting viewingfrequencies of the contents from a content viewing history storage,totaling viewing frequencies of the contents determined to be theidentical/derived contents from said input viewing history informationof said plurality of the contents and said identical/derived contentgrouping information, calculating a total viewing frequency for eachidentical/derived content, and determining topicality of saididentical/derived content based upon said total viewing frequency.
 10. Acontent topicality determination method according to claim 9: whereinsaid content has a time axis; wherein said content grouping step groupsidentical/derived temporal segments included in said plurality of thecontents based on the result of said collation, and calculates saididentical/derived content grouping information; and wherein saidtopicality determination step calculates said total viewing frequencyfor each of said identical/derived temporal segments, and determines thetopicality for each of said identical/derived temporal segments.
 11. Acontent topicality determination method according to claim 10, whereinsaid content is music or video.
 12. A content topicality determinationmethod according to claim 11, wherein said feature of the contentincludes at least one of a visual feature and an audio feature.
 13. Acontent topicality determination method according to claim 9, whereinsaid topicality determination step determines that the content of whichsaid total viewing frequency is large is a topical content.
 14. Acontent topicality determination method according to claim 13, whereinsaid topicality determination step determines the topicality of thecontent by a temporal segment.
 15. A content topicality determinationmethod according to claim 9, comprising a content viewing step of thatselecting the content from among said plurality of the contents, viewingthe content, and outputting identification information and a viewingsegment of the viewed content as said content viewing historyinformation.
 16. A content topicality determination method according toclaim 9, wherein said topicality determination step: determines only thehistory meeting a constant viewing condition to be valid from saidcontent viewing history information; and calculates the total viewingfrequency by using said section determined to be valid.
 17. Anon-transitory computer readable storage medium storing a contenttopicality determination program for causing an information processingapparatus to execute: a feature extraction process of extractingfeatures for each of said plurality of the contents; a content groupingprocess of collating the features of said plurality of the contentsextracted by said feature extractor with each other, grouping identicalcontents and the derived contents produced by using the above identicalcontents to be included in said plurality of the contents, based on theresult of said collation, and calculating identical/derived contentgrouping information; and a topicality determination process of totalingviewing frequencies of the contents determined to be theidentical/derived contents from viewing history information of saidplurality of the contents and said identical/derived content groupinginformation, calculating a total viewing frequency for eachidentical/derived content, and determining topicality of saididentical/derived content based upon said total viewing frequency.